Xiaoli Ma (Previous PI Geoffrey Ye Li)

Professor @ Georgia Tech

NSF

Improving Spectrum Efficiency for Hyper-Dense IoT Networks

Award Number: 1815637

Project Descriptions

The emerging Internet of things (IoT) technology will enable a whole new set of applications, imposing far reaching influences on multifarious aspects of the society. At the same time, it also poses grand challenges to the wireless industry, such as the difficulty of allocating sufficient control/data channel resources to a large number of IoT devices operating in the same spectrum. In this proposal, the PIs will develop novel physical (PHY), medium access control (MAC), and core network IoT architectures and algorithms to enable massive IoT deployments. A comprehensive, cross-layer study into joint equalizer and waveform designs, graph based radio resource management, and traffic pattern based core network scheduling will be pursued to improve the spectral efficiency of hyper-dense IoT scenarios.

Synopsis

The intellectual merit of this project lies in its holistic plan for cross-layer investigation into PHY, MAC, and core network issues for enabling massive IoT deployment in future wireless networks. In particular, this project aims at tackling the following challenges:

  • System-Centric Waveform Design for Massive IoT,
  • Graph-based Radio Resource Management,
  • Core network Connection Efficiency.

 

Personnel

  • Faculty
    1. Dr. Xiaoli Ma
    2. Dr. Ismail Guvenc (North Carolina State University)
    3. Dr. Eyuphan Bulut (Virginia Commonwealth University)
  • Graduate Students
    1. Hao Ye (graduated)
    2. Ziyan He
    3. Kaiwen Zheng

 

Collaborators

  • North Carolina State University
  • Virginia Commonwealth University
  • Dr. Geoffrey Ye Li (Imperial College London)

 

Publications

  1. Z.-J. Qin, F. Y. Li, G. Y. Li, J. A. McCann, and Q. Ni, “Low-power wide-area networks for green IoT,” to appear in IEEE Wireless Communications.
  2. X.-W. Zhou, M.-X. Sun, G. Y. Li, and B.-H. F. Juang, “Intelligent wireless communications enabled by cognitive radio and machine learning,” to appear in China Communications.
  3. A. Frϕytlog, T. Foss, O. Bakker, G. Jevne, M. A. Haglund, F. Y. Li, J. Oller, and G. Y. Li, “Ultra-low power wake-up radio for 5G IoT,” to appear in IEEE Communications Magazine.
  4. Q.-Y. Hu, Y.-L. Cai, G.-D. Yu, Z.-J. Qin, M.-J. Zhao, and G. Y. Li, “Joint computation offloading and trajectory design for UAV-enabled mobile edge computing systems,” to appear in IEEE Internet of Things Journal.

 

Broader Impacts

The research results have been disseminated to the communities of wireless communication area through high-quality journal and conference publications. The research findings are likely to significantly improve the design of PHY, MAC, and core network IoT architecture.

Educational Activities

The PI has highly committed to teaching and integrating research with STEM education. The PI has restructured wireless communication courses currently taught to engage students in more hands-on projects comprised of intensive experiments and programming with an emphasis on massive IoT.